Optimizing Cyber Insurance and Defense for Multi-Energy Systems under False Data Injections

This paper introduces a novel cyber insurance planning model specifically designed to enhance the resilience of ICT-integrated multi-energy systems (MES) against cyber threats, particularly False Data Injection (FDI) attacks. The proposed Hierarchical Cyber Insurance Planning Model (HCIPM) offers an integrated approach to managing the dual challenges of financial risk and operational disruptions caused by sophisticated cyber-attacks. The model is built upon a twostage hierarchical optimization framework: the first stage determines the optimal allocation of cyber insurance to minimize costs while ensuring adequate risk coverage, and the second stage focuses on real-time operational defense strategies, such as load shedding and resource management, to mitigate the impact of cyber incidents. A key innovation of the HCIPM is its incorporation of a Distributionally Robust Optimization (DRO) methodology, combined with Conditional Value at Risk (CVaR), to effectively handle the uncertainties inherent in FDI attack scenarios. By representing extreme events and their probabilities, this framework ensures robust decision-making under high uncertainty. Extensive simulations conducted on a 33-20 node distribution system demonstrate the efficacy of the proposed model. Results indicate that the HCIPM achieves a 35\% reduction in load shedding costs and a 28\% improvement in resilience metrics, such as system recovery time and operational continuity, compared to traditional approaches.